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93 lines
3.0 KiB
Markdown
93 lines
3.0 KiB
Markdown
# OCR - Optical Character Recognition
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This software implements a heavily parallelized pipeline to recognize text in PDF files. It is used for nopaque's OCR service but you can also use it standalone, for that purpose a convenient wrapper script is provided.
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## Software used in this pipeline implementation
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- Official Debian Docker image (buster-slim): https://hub.docker.com/_/debian
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- Software from Debian Buster's free repositories
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- ocropy (1.3.3): https://github.com/ocropus/ocropy/releases/tag/v1.3.3
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- pyFlow (1.1.20): https://github.com/Illumina/pyflow/releases/tag/v1.1.20
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- Tesseract OCR (4.1.1): https://github.com/tesseract-ocr/tesseract/releases/tag/4.1.1
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- tessdata_best (4.1.0): https://github.com/tesseract-ocr/tessdata_best/releases/tag/4.1.0
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## Use this image
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1. Create input and output directories for the pipeline.
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``` bash
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mkdir -p /<my_data_location>/input /<my_data_location>/output
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```
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2. Place your PDF files inside `/<my_data_location>/input`. Files should all contain text of the same language.
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3. Start the pipeline process. Check the [Pipeline arguments](#pipeline-arguments) section for more details.
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```
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# Option one: Use the wrapper script
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## Install the wrapper script (only on first run). Get it from https://gitlab.ub.uni-bielefeld.de/sfb1288inf/ocr/-/raw/development/wrapper/ocr, make it executeable and add it to your ${PATH}
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cd /<my_data_location>
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ocr -i input -l <language_code> -o output <optional_pipeline_arguments>
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# Option two: Classic Docker style
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docker run \
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--rm \
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-it \
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-u $(id -u $USER):$(id -g $USER) \
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-v /<my_data_location>/input:/input \
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-v /<my_data_location>/output:/output \
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gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:development \
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-i /ocr_pipeline/input \
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-l <language_code> \
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-o /ocr_pipeline/output \
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<optional_pipeline_arguments>
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```
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4. Check your results in the `/<my_data_location>/output` directory.
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### Pipeline arguments
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#### Mandatory arguments
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`-i, --input-dir INPUT_DIR`
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* Input directory
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`-o, --output-dir OUTPUT_DIR`
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* Output directory
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`-l, --language {spa,fra,dan,deu,eng,frm,chi_tra,ara,enm,ita,ell,frk,rus,por}`
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* Language of the input (3-character ISO 639-2 language codes)
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#### Optional arguments
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`--binarize`
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* Add binarization as a preprocessing step
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`--log-dir`
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* Logging directory
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`--mem-mb`
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* Amount of system memory to be used (Default: min(--n-cores * 2048, available system memory))
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`--n-cores`
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* Number of CPU threads to be used (Default: min(4, available CPU cores))
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`-v, --version`
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* Returns the current version of the OCR pipeline
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``` bash
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# Example with all arguments used
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docker run \
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--rm \
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-it \
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-u $(id -u $USER):$(id -g $USER) \
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-v /<my_data_location>/input:/ocr_pipeline/input \
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-v /<my_data_location>/output:/ocr_pipeline/output \
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-v /<my_data_location>/logs:/ocr_pipeline/logs \
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gitlab.ub.uni-bielefeld.de:4567/sfb1288inf/ocr:development \
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-i /ocr_pipeline/input \
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-l eng \
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-o /ocr_pipeline/output \
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--binarize \
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--log-dir /ocr_pipeline/logs \
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--n-cores 8 \
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```
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